BEYİN MR GÖRÜNTÜLERİNDEN TÜMÖR TESPİTİ İÇİN DERİN ÖĞRENMEYE DAYALI HİBRİT MODELLER

İclal Özcan; Serkan Öztürk

doi:10.17780/ksujes.1293378

EN TR

DEEP LEARNING BASED HYBRID MODELS FOR TUMOR DETECTION FROM BRAIN MR IMAGES

Abstract

An abnormal proliferation of human cells due to excessive division is called a tumor. Tumors, which can form in many parts of the body, have a degree of danger according to where they occur. The brain is one of the most dangerous areas of tumor formation. Intense studies have been carried out in recent years for the detection of tumors in the brain region. Artificial intelligence-based methods are at the forefront of these studies. Convolutional neural networks (CNN), a deep learning method, are used for classification, feature extraction and transfer learning purposes. In this study, CNN method was used for feature extraction from brain MR images. In this context, DarkNet53 model, one of the pre-trained CNN models, was selected for feature extraction. The feature extractor layers of the DarkNet53 model are conv52, res23, avg1, and conv53, respectively. After feature extraction, feature selection process was applied. Relief and Chi-Square Test methods were chosen as feature-selective methods. After feature extraction, the support vector machine algorithm, which is one of the classical machine learning methods, was determined as the classifier method. The proposed method has been tested on the “Brain MRI Images for Brain Tumor Detection” dataset. According to the experimental results, the best result was obtained with the proposed method in which the res23 layer was determined as feature extractor and the Chi-Square Test method as feature selective.

Keywords

BEYİN MR GÖRÜNTÜLERİNDEN TÜMÖR TESPİTİ İÇİN DERİN ÖĞRENMEYE DAYALI HİBRİT MODELLER

Öz

İnsan hücrelerinin aşırı bölünmeye bağlı anormal bir şekilde çoğalmasına tümör denmektedir. Vücudun birçok noktasında oluşabilen tümörler, oluştuğu yere göre tehlikelilik derecesine sahiptir. Beyin, tümör oluşumunda en tehlikeli bölgelerden birisidir. Beyin bölgesindeki tümörlerin tespiti için son yıllarda yoğun çalışmalar gerçekleştirilmiştir. Yapay zeka temelli yöntemler bu çalışmaların başında gelmektedir. Bir derin öğrenme yöntemi olan evrişimli sinir ağları (CNN) sınıflandırma, özellik çıkarma ve transfer öğrenme amaçlarıyla kullanılmaktadır. Bu çalışmada CNN yöntemi beyin MR görüntülerinden özellik çıkarma amacıyla kullanılmıştır. Bu kapsamda ön eğitimli CNN modellerinden DarkNet53 modeli özellik çıkarımı için seçilmiştir. DarkNet53 modelinin özellik çıkarıcı katmanları sırasıyla conv52, res23, avg1 ve conv53 katmanlarıdır. Özellik çıkarımından sonra özellik seçimi işlemi uygulanmıştır. Relief ve Ki-Kare Test yöntemleri özellik seçici yöntemler olarak seçilmiştir. Özellik çıkarımından sonra klasik makine öğrenme yöntemlerinden birisi olan destek vektör makineleri algoritması sınıflandırıcı yöntem olarak belirlenmiştir. Önerilen yöntem, “Brain MRI Images for Brain Tumor Detection” veri seti üzerinde denenmiştir. Deneysel sonuçlara göre: res23 katmanının özellik çıkarıcı, Ki-Kare Test yönteminin özellik seçici olarak belirlendiği önerilen yöntemle en iyi sonuç elde edilmiştir.

Anahtar Kelimeler

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Bilgisayar Yazılımı

Bölüm

Araştırma Makalesi

Yazarlar

İclal Özcan ^*
0000-0001-7414-7425
Türkiye

Serkan Öztürk
0000-0002-0309-3420
Türkiye

Yayımlanma Tarihi

3 Eylül 2023

Gönderilme Tarihi

6 Mayıs 2023

Kabul Tarihi

19 Temmuz 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 26 Sayı: 3

DOI

https://doi.org/10.17780/ksujes.1293378

IZ

https://izlik.org/JA42ZD65TC

Kaynak Göster

RIS / Bibtex

APA

Özcan, İ., & Öztürk, S. (2023). BEYİN MR GÖRÜNTÜLERİNDEN TÜMÖR TESPİTİ İÇİN DERİN ÖĞRENMEYE DAYALI HİBRİT MODELLER. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(3), 718-733. https://doi.org/10.17780/ksujes.1293378